The advent of AI vision systems is set to revolutionize crop management, promising significant advancements in disease detection and overall agricultural efficiency. At the forefront of this technological evolution is AlamedaDev, an AI firm based in Barcelona, Spain. In an exclusive interview, Osledy Bazó, the founder and CTO of AlamedaDev, shared insights into the progress, benefits, and potential risks associated with these cutting-edge systems.
AI vision systems are transforming the agricultural landscape by enabling early detection of crop diseases. These systems, combined with new leaf health sensor technologies, can identify signs of infection before they become visible to the human eye. This early detection allows farmers to take timely action, potentially saving entire crops from devastation. Moreover, AI crop disease detection systems can alert farmers when a set ‘infection threshold’ is reached, recommending the appropriate fungicide or treatment and determining the optimal weather conditions for application.
“This feature primarily enhances the efficiency of disease detection in crops by assisting in the rapid and automated classification,” Bazó explained. “But this is not just about recognizing problems that the human eye might miss. It is about speeding up the laborious task of identifying and documenting various aspects of crop health issues. The AI system records the detection date, type of disease or pest, potential impact on the crop, and the spread rate.”
The benefits of AI in agriculture extend beyond disease detection. By automating the detection and recording processes, AI technology reduces the time and resources spent on manual scouting, allowing growers to focus on other critical tasks. Rapid detection capabilities ensure that diseases are managed before they become severe, facilitating quicker responses such as the application of pesticides or fungicides.
Looking ahead, Bazó envisions a future where AI systems could directly control drones to apply treatments precisely where and when needed, based on established severity thresholds. This level of automation could revolutionize crop disease management, making it more efficient and less labor-intensive.
However, the journey to full-scale implementation is not without challenges. The first season for a grower using an AI crop disease detection system would be highly customized to their specific operational processes and needs. The financial viability and scale of the technology deployment would depend on the specific crops and the extent of the cultivated area. AlamedaDev’s role includes helping clients identify and prioritize their needs based on realistic assessments, rather than pursuing overly ambitious implementations from the start.
Training personnel to achieve a high level of accuracy with the AI system before full-scale implementation is crucial. “The initial steps toward integrating an AI disease detection system involve establishing clear priorities and operational parameters tailored to the client’s specific agricultural context,” Bazó noted. “Developing the technology to suit particular crops is crucial, as is training the AI model with real crop data to ensure accurate disease detection and classification.”
Despite the promising benefits, there are potential risks associated with relying on AI for crop disease detection. While AI significantly increases efficiency and accuracy, it is fundamentally a tool that supports, rather than replaces, skilled labor in agriculture. Farmers and agricultural workers must continue to engage actively with the processes, verifying that the AI’s findings are integrated thoughtfully into broader farm-management practices.
As for the availability of this technology, Bazó revealed that AlamedaDev’s Vision Transformer (ViT) AI model is currently at the proof-of-concept stage with an olive-farming client in Andalusia. The ViT AI model was trained on a vast dataset of crop leaf images labeled with specific disease markers. During this phase, field workers helped train ViT by sending it images as they manually inspected crops. The project is progressing, focusing on validating the technology’s effectiveness through smartphone-captured images before expanding to more sophisticated data capture methods like drones.
In Europe, significant efforts are being made to promote the adoption of disruptive technologies across various sectors, including agriculture. In Spain, there is a strong push to encourage companies to integrate AI into their operations through innovative public procurement. These initiatives align with larger European Union programs, such as the Next Generation EU, aiming to foster a more resilient and digital Europe.
AlamedaDev’s progress in developing AI systems for agriculture highlights the potential for transformative change in crop management. However, the successful integration of these technologies will require careful planning, training, and ongoing collaboration between AI developers and agricultural professionals.